Breast cancer is one of the most frequently occurring cancers among women in the United States and Europe. Early, accurate detection is one of the best defenses against cancer.
Of course, there are a variety of methods and systems for automatically detecting breast lesions and tumors in images from ultrasound, digital and analog mammograms and MRI images. See US2005/0027188. It is known that contrast agents when passing through tumors behave differently than when the contrast agent passes through ordinary tissue (see U.S. Pat. No. 6,553,327), which aids or facilitates diagnosis of cancer.
Unfortunately, many women who suspect they have breast cancer just undergo routine screening mammograms, diagnostic mammograms and ultrasound, and end up without definitive diagnosis because mammography and ultrasound both depend on varying density of tissue (i.e., that tumor is a different density than normal breast parenchyma). Both mammography and ultrasound determine image contrast based on the varying density of tissue. Tumors may be found in this way, as they have different densities than normal breast parenchyma. When there is a suspicion that something is wrong with the breast, women and men both undergo biopsy to confirm this suspicion of malignancy with histopathology. Subsequently, upon resection, the results have shown when differences in tissue densities were detected that mammograms consistently underestimate the extent of the malignancy and may also misrepresent the location of the malignancies.
Thus, if the mammographer or gynecologist is uncomfortable with these estimates, it is now typical for patients to be subsequently referred for MRI scanning of their breast. Unfortunately, as many as 30% of breast lesions are missed during the mammography screening process. Additionally, when radiologists classify mammograms as being suspicious and biopsies are performed to confirm malignancy, less than one third of mammographically-identified suspicious lesions are found positive. The actual 3D location of these tumors may be one cause of these unacceptable “misses,” as when the needle biopsy is performed in the wrong location.
Unlike mammograms, contrast differences in MRI images do not depend upon tissue density differences, but upon differing proton distributions, and with contrast agents, cell permeability and other factors such as angiogenesis play a role. However, the discriminative characteristics of the MRI that seem to matter most in the accurate detection or differentiation of cancer are linked to certain dynamic and static parameters. Both these dynamic and static parameters are exceedingly difficult for human eyes to discern because: 1) the dynamic factors depend on temporal factors, i.e., detecting whether a region of an image is getting brighter in intensity over time or not, and whether that affected specific region of interest (ROI) is contiguous with other regions that are doing the same thing over time, or not. Answering whether the malignancy is one focus or multiple foci is immensely challenging to the naked eye. Without a better way to detect malignancies, the substantially greater cost of MRIs means that mammograms remain the first step in early detection, despite its known limitations.
Many existing systems allow humans to see 2D slices of intensity images, but both the dynamic and static parameters in many display systems are exceedingly difficult for humans to discern because: (1) the dynamic parameters depend on whether or not the intensity of a particular point in the image is increasing over time, and (2) the static parameters are defined by contiguous groups of points, called regions, that exhibit similar characteristics. Due to the large amount of image data produced from scans like MRIs, determining whether or not a malignancy has one focus or multiple foci is immensely challenging. Manually searching for potential malignancies in images can be a time-consuming and error-prone task.
Image processing can be used to automate this task. Some automated and semi-automated approaches to finding cancer in images use texture or intensity information from a single image taken at a particular time. However, using a single intensity for each image point is often insufficient for reliably determining the presence or absence and extent of cancer.
Other approaches have tried to identify regions in 2D images and use shape information to determine the likelihood of cancer. However, considering only individual, fixed-axis 2D slices can cause algorithms to miss regions that exhibit certain shape characteristics in other axes.
Volume rendering can also be used to display 3D image data. Direct volume rendering (DVR) is an approach that displays all image data simultaneously, which is useful for visualizing image data that does not contain distinct, easily-distinguished features. Other approaches include surface rendering, which shows the surface of only certain distinct image features. Another widely-used approach is maximum intensity projection (MIP) rendering, which shows only the highest intensity values projected along a view axis through the volume.
Another problem with existing approaches for finding malignancies is that they focus primarily on determining the existence and location of the malignancy without determining the type of malignancy. Malignancies of different types exhibit similar patterns, requiring intensity information over time from images and predetermined parameters to classify malignancies. However, using insufficient time points may limit the effectiveness of discrimination.
Further, examining thousands of images (bilateral breast cases consists of approximately 1,000 or more images), and dividing these hundreds of images into sub-regions, comprising tens of thousands of comparisons, is tedious and error prone. The human eye is not adept at “fine-grained” temporal discrimination, and the human brain is easily fatigued by such a difficult task. The human eye of the radiologist or surgeon, for example, does not retain intensity difference well over time nor detect them well. If it did, the result would be visual “smearing” of scenes.
There remains, therefore, a need for a better approach to image processing for evaluation of tumors, lesions, and other abnormalities. A desirable approach would use both intensity-over-time information and region information in up to three spatial dimensions. It would also use known characteristics to automatically evaluate the sizable image data. Because breast images consist of soft tissue without distinct features, use of approaches like DVR in this context would be suitable, because it could show identified malignancies in the entire image in addition to the breast tissue. Just such an approach is now possible and described below.